Abstract: Real-time mixed-criticality systems have stringent timing requirements in the form of hard deadlines and a collection of tasks having different levels of importance or criticality hosted on a single hardware platform. Avionics and automotive are two well known domains for such systems, where the criticality level has a strong correlation with the assurance levels used for certification. Traditionally, static processor partitioning, in the form of fixed allocation of processing time, has been employed to ensure isolation between the different criticality tasks and guarantee task deadlines. However, due to increasing software and hardware complexity, pessimistic upper-bounds are often used for the worst-case execution time estimates of critical tasks, and this leads to significant processor under-utilisation. To overcome this inefficiency, the concept of mixed-criticality scheduling has emerged in the last decade. Under this paradigm, processing capacity is partitioned among all the tasks using a less conservative execution time estimate. In the eventuality that some critical task requires additional execution budget, the schedule is adapted to favour the critical tasks over less critical ones.
Focusing on mixed-criticality scheduling issues, in this talk I will present two recent results. Considering a single-core processor, I will present a new scheduling model and runtime budget enforcement policy to dynamically manage the budget allocations for critical tasks so that: 1) all tasks continue to receive as much budget as they need for as long as feasible, and 2) less critical tasks continue to receive some guaranteed budget even after the schedule is adapted to favour the critical tasks. Then, considering a multi-core processor, I will present a new fluid scheduling model that significantly improves the schedulability performance when compared to state-of-the-art approaches, while still having a theoretically bounded performance guarantee. I will conclude the talk highlighting some of the open problems in this area.

Speaker Profile: Arvind Easwaran is an Assistant Professor in the School of Computer Science and Engineering at Nanyang Technological University (NTU), Singapore. He received a PhD degree from the University of Pennsylvania, USA, in 2008. Prior to joining NTU in 2013, he has been a Scientist at the Polytechnic Institute of Porto, Portugal and at Honeywell Aerospace, USA. He is currently leading research projects on topics related to cyber-physical systems including real-time scheduling theory for mixed-criticality systems, resilient cyber-infrastructure for smart manufacturing and decentralised algorithm design for smart electricity meters. His research interests are in the design and analysis of cyber-physical and real-time systems.